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VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program.

Yan, Yijun; Zhao, Sophia; Fang, Yuxi; Liu, Yuren; Chen, Zhongxin; Ren, Jinchang

Authors

Sophia Zhao

Yuxi Fang

Yuren Liu

Zhongxin Chen



Contributors

Amir Hussain
Editor

Huimin Zhao
Editor

Kaizhu Huang
Editor

Jiangbin Zheng
Editor

Jun Cai
Editor

Rongjun Chen
Editor

Yinyin Xiao
Editor

Abstract

In this paper, we introduce a new concept in VIP-STB, a funded project through Agri-Tech in China: Newton Network+ (ATCNN), in developing feasible solutions towards scaling-up STB from village level to upper level via some generic models and systems. There are three tasks in this project, i.e. normalized difference vegetation index (NDVI) estimation, wheat density estimation and household-based small farms (HBSF) engagement. In the first task, several machine learning models have been used to evaluate the performance of NDVI estimation. In the second task, integrated software via Python and Twilio is developed to improve communication services and engagement for HBSFs, and provides technical capabilities. In the third task, crop density/population is predicted by conventional image processing techniques. The objectives and strategy for VIP-STB are described, experimental results on each task are presented, and more details on each model that has been implemented are also provided with future development guidance.

Citation

YAN, Y., ZHAO, S., FANG, Y., LIU, Y., CHEN, Z. and REN, J. 2020. VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program. In Ren, J., Hussain, A., Zhao, H., Huang, K., Zheng, J., Cai, J., Chen, R. and Xiao, Y. (eds.). 2020. Advances in brain inspired cognitive systems: proceedings of the 10th Brain inspired cognitive systems (BCIS) international conference 2019 (BCIS 2019), 13-14 July 2019, Guangzhou, China. Lecture notes in computer science, 11691. Cham: Springer [online], pages 283-292. Available from: https://doi.org/10.1007/978-3-030-39431-8_27

Conference Name 10th Brain inspired cognitive systems (BICS) international conference 2019 (BICS 2019)
Conference Location Guangzhou, China
Start Date Jul 13, 2019
End Date Jul 14, 2019
Acceptance Date Jul 10, 2019
Online Publication Date Jul 14, 2019
Publication Date Feb 1, 2020
Deposit Date Jun 21, 2022
Publicly Available Date Mar 29, 2024
Publisher Springer
Volume 11691
Pages 283-292
Series Title Lecture notes in computer science
Book Title Advances in brain inspired cognitive systems: proceesings of 10th Brain inspired cognitive systems (BICS) international conference 2019 (BICS 2019), 13-14 July 2019, Guangzhou, China
ISBN 9783030394301
DOI https://doi.org/10.1007/978-3-030-39431-8_27
Keywords Precision agriculture; Machine learning; Information fusion
Public URL https://rgu-repository.worktribe.com/output/1650153

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Copyright Statement
This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-39431-8_27. This accepted manuscript is subject to Springer Nature's AM terms of use.




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